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Supervised Learning : Convolutional Neural Network Frameworks for Multi-Class Image Classification

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John Pauline Pineda

December 30, 2023

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• 1. Table of Contents
  • 1.1 Data Background
  • 1.2 Data Description
  • 1.3 Data Quality Assessment
  • 1.4 Data Preprocessing
    • 1.4.1 Image Description
    • 1.4.2 Image Augmentation
  • 1.5 Data Exploration
    • 1.5.1 Exploratory Data Analysis
  • 1.6 Model Development
    • 1.6.1 Premodelling Data Description
    • 1.6.2 CNN With No Regularization
    • 1.6.3 CNN With Dropout Regularization
    • 1.6.4 CNN With Batch Normalization Regularization
    • 1.6.5 CNN With Dropout and Batch Normalization Regularization
  • 1.7 Consolidated Findings
• 2. Summary
• 3. References

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1. Table of Contents

This project explores the various convolutional neural network (CNN) frameworks for processeing images through convolutional, activation, pooling, and fully connected layers, capturing hierarchical features and learning to map input images to their respective classes during training using various helpful packages in Python. Various CNN architectures applied in the analysis to learn features and patterns at different levels of abstraction in images included CNN Without Regularization, CNN With Dropout Regularization, CNN With Batch Normalization Regularization and CNN With Dropout and Batch Normalization Regularization. The different CNN algorithms were evaluated using the categorical cross entropy loss which measures the difference between the predicted probability distribution and the true distribution of the class labels. Model multi-classification performance was measured using Accuracy, Precision, Recall and F1 Score. All results were consolidated in a Summary presented at the end of the document.

A convolutional neural network model is a type of neural network architecture specifically designed for image classification and computer vision tasks by automatically learning hierarchical features directly from raw pixel data. The core building block of a CNN is the convolutional layer. Convolution operations apply learnable filters (kernels) to input images to detect patterns such as edges, textures, and more complex structures. The layers systematically learn hierarchical features from low-level (e.g., edges) to high-level (e.g., object parts) as the network deepens. Filters are shared across the entire input space, enabling the model to recognize patterns regardless of their spatial location. After convolutional operations, an activation function is applied element-wise to introduce non-linearity and allow the model to learn complex relationships between features. Pooling layers downsample the spatial dimensions of the feature maps, reducing the computational load and the number of parameters in the network - creating spatial hierarchy and translation invariance. Fully connected layers process the flattened features to make predictions and produce an output vector that corresponds to class probabilities using an activation function. The CNN is trained using backpropagation and optimization algorithms. A loss function is used to measure the difference between predicted and actual labels. The network adjusts its weights to minimize this loss. Gradients are calculated with respect to the loss, and the weights are updated accordingly through a backpropagation mechanism.

1.1. Data Background

A subset of an open COVID-19 Radiography Dataset from Kaggle (with all credits attributed to Preet Viradiya, Juliana Negrini De Araujo, Tawsifur Rahman, Muhammad Chowdhury and Amith Khandakar) was used for the analysis as consolidated from the following primary sources:

1. Covid19 X-Ray Images from BIMCV Medical Imaging Databank of the Valencia Region
2. Covid19 X-Ray Images from GitHub: ML Group
3. Covid19 X-Ray Images from Italian Society of Medical and Interventional Radiology
4. Covid19 X-Ray Images from European Society of Radiology
5. Covid19 X-Ray Images from GitHub: Joseph Paul Cohen
6. Covid19 X-Ray Images from Publication: COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep Learning
7. Pneumonia and Normal X-Ray Images from Kaggle: RSNA Pneumonia Detection Challenge
8. Pneumonia and Normal X-Ray Images from Kaggle: Chest X-Ray Images (Pneumonia)

This study hypothesized that images contain a hierarchy of features which allows the differentiation and classification across various image categories.

The target variable for the study is:

• CLASS - Multi-categorical diagnostic classification for the x-ray images

The hierarchical representation of image features enables the network to transform raw pixel data into a meaningful and compact representation, allowing it to make accurate predictions during image classification. The different features automatically learned during the training process are as follows:

• LOW-LEVEL FEATURES - Edges and textures
• MID-LEVEL FEATURES - Patterns and shapes
• HIGH-LEVEL FEATURES - Object parts
• ABSTRACT FEATURES - Object semantics
• SEMANTIC CONCEPTS - Object categories
• HIERARCHICAL REPRESENTATION - Spatial hierarchy
• ROTATION | SCALE INVARIANCE - Invariant features
• LOCALIZATION INFORMATION - Spatial localization

1.2. Data Description

1. The dataset is comprised of:
   • 3600 images (observations)
   • 1 target (variable)
     • CLASS: COVID = 1200 images
     • CLASS: Normal = 1200 images
     • CLASS: Viral Pneumonia = 1200 images

##################################
# Installing important packages
##################################
# !pip install mlxtend
# !pip install --upgrade tensorflow
# !pip install opencv-python
# !pip install keras==2.12.0

##################################
# Loading Python Libraries 
# for Data Loading,
# Data Preprocessing and
# Exploratory Data Analysis
##################################
import numpy as np
import pandas as pd 
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
%matplotlib inline
import tensorflow as tf
import keras
from PIL import Image
from glob import glob
import cv2
import os
import random

WARNING:tensorflow:From C:\Users\John pauline magno\AppData\Roaming\Python\Python311\site-packages\keras\losses.py:2664: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.

##################################
# Loading Python Libraries 
# for Model Development
##################################
from keras import backend as K
from keras import regularizers
from keras.models import Sequential, Model,load_model
from keras.layers import Activation, Dense, Dropout, Flatten, Conv2D, MaxPooling2D, MaxPool2D, AveragePooling2D, GlobalMaxPooling2D, BatchNormalization
from keras.wrappers.scikit_learn import KerasClassifier
from keras.utils.np_utils import to_categorical
from keras.optimizers import Adam, SGD
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint
from tensorflow.keras.utils import img_to_array, array_to_img

##################################
# Loading Python Libraries 
# for Model Evaluation
##################################
from keras.metrics import PrecisionAtRecall, Recall 
from sklearn.metrics import confusion_matrix
from sklearn.metrics import precision_recall_fscore_support, accuracy_score

##################################
# Setting random seed options
# for the analysis
##################################
def set_seed(seed=88888888):
    np.random.seed(seed) 
    tf.random.set_seed(seed) 
    keras.utils.set_random_seed(seed)
    random.seed(seed)
    tf.config.experimental.enable_op_determinism()
    os.environ['TF_DETERMINISTIC_OPS'] = "1"
    os.environ['TF_CUDNN_DETERMINISM'] = "1"
    os.environ['PYTHONHASHSEED'] = str(seed)

set_seed()

##################################
# Loading the dataset
##################################
path = 'C:/Users/John pauline magno/Python Notebooks/COVID-19_Radiography_Dataset/'

##################################
# Defining the image category levels
##################################
diagnosis_code_dictionary = {'COVID': 0,
                             'Normal': 1,
                             'Viral Pneumonia': 2}

##################################
# Defining the image category descriptions
##################################
diagnosis_description_dictionary = {'COVID': 'Covid-19',
                                    'Normal': 'Healthy',
                                    'Viral Pneumonia': 'Viral Pneumonia'}

##################################
# Consolidating the image path
##################################
imageid_path_dictionary = {os.path.splitext(os.path.basename(x))[0]: x for x in glob(os.path.join(path, '*','*.png'))}

##################################
# Taking a snapshot of the dictionary
##################################
dict(list(imageid_path_dictionary.items())[0:5]) 

{'COVID-1': 'C:/Users/John pauline magno/Python Notebooks/COVID-19_Radiography_Dataset\\COVID\\COVID-1.png',
 'COVID-10': 'C:/Users/John pauline magno/Python Notebooks/COVID-19_Radiography_Dataset\\COVID\\COVID-10.png',
 'COVID-100': 'C:/Users/John pauline magno/Python Notebooks/COVID-19_Radiography_Dataset\\COVID\\COVID-100.png',
 'COVID-1000': 'C:/Users/John pauline magno/Python Notebooks/COVID-19_Radiography_Dataset\\COVID\\COVID-1000.png',
 'COVID-1001': 'C:/Users/John pauline magno/Python Notebooks/COVID-19_Radiography_Dataset\\COVID\\COVID-1001.png'}

##################################
# Consolidating the information
# from the dataset
# into a dataframe
##################################
xray_images = pd.DataFrame.from_dict(imageid_path_dictionary, orient = 'index').reset_index()
xray_images.columns = ['Image_ID','Path']
classes = xray_images.Image_ID.str.split('-').str[0]
xray_images['Diagnosis'] = classes
xray_images['Target'] = xray_images['Diagnosis'].map(diagnosis_code_dictionary.get) 
xray_images['Class'] = xray_images['Diagnosis'].map(diagnosis_description_dictionary.get) 

##################################
# Performing a general exploration of the dataset
##################################
print('Dataset Dimensions: ')
display(xray_images.shape)

Dataset Dimensions: 

(3600, 5)

##################################
# Listing the column names and data types
##################################
print('Column Names and Data Types:')
display(xray_images.dtypes)

Column Names and Data Types:

Image_ID     object
Path         object
Diagnosis    object
Target        int64
Class        object
dtype: object

##################################
# Taking a snapshot of the dataset
##################################
xray_images.head()

	Image_ID	Path	Diagnosis	Target	Class
0	COVID-1	C:/Users/John pauline magno/Python Notebooks/C...	COVID	0	Covid-19
1	COVID-10	C:/Users/John pauline magno/Python Notebooks/C...	COVID	0	Covid-19
2	COVID-100	C:/Users/John pauline magno/Python Notebooks/C...	COVID	0	Covid-19
3	COVID-1000	C:/Users/John pauline magno/Python Notebooks/C...	COVID	0	Covid-19
4	COVID-1001	C:/Users/John pauline magno/Python Notebooks/C...	COVID	0	Covid-19

##################################
# Performing a general exploration of the numeric variables
##################################
print('Numeric Variable Summary:')
display(xray_images.describe(include='number').transpose())

Numeric Variable Summary:

	count	mean	std	min	25%	50%	75%	max
Target	3600.0	1.0	0.81661	0.0	0.0	1.0	2.0	2.0

##################################
# Performing a general exploration of the object variable
##################################
print('Object Variable Summary:')
display(xray_images.describe(include='object').transpose())

Object Variable Summary:

	count	unique	top	freq
Image_ID	3600	3600	COVID-1	1
Path	3600	3600	C:/Users/John pauline magno/Python Notebooks/C...	1
Diagnosis	3600	3	COVID	1200
Class	3600	3	Covid-19	1200

##################################
# Performing a general exploration of the target variable
##################################
xray_images.Diagnosis.value_counts()

COVID              1200
Normal             1200
Viral Pneumonia    1200
Name: Diagnosis, dtype: int64

##################################
# Performing a general exploration of the target variable
##################################
xray_images.Diagnosis.value_counts(normalize=True)

COVID              0.333333
Normal             0.333333
Viral Pneumonia    0.333333
Name: Diagnosis, dtype: float64

1.3. Data Quality Assessment

Data quality findings based on assessment are as follows:

1. Details
   • 1.1 Details
     • 1.1.1 Details
       • 1.1.1.1 Details

##################################
# Counting the number of duplicated images
##################################
xray_images.duplicated().sum()

0

##################################
# Gathering the number of null images
##################################
xray_images.isnull().sum()

Image_ID     0
Path         0
Diagnosis    0
Target       0
Class        0
dtype: int64

1.4. Data Preprocessing

1.4.1 Image Description

1. Details
   • 1.1 Details
     • 1.1.1 Details
       • 1.1.1.1 Details

##################################
# Including the pixel information
# of the actual images
# in array format
# into a dataframe
##################################
xray_images['Image'] = xray_images['Path'].map(lambda x: np.asarray(Image.open(x).resize((75,75))))

##################################
# Listing the column names and data types
##################################
print('Column Names and Data Types:')
display(xray_images.dtypes)

Column Names and Data Types:

Image_ID     object
Path         object
Diagnosis    object
Target        int64
Class        object
Image        object
dtype: object

##################################
# Taking a snapshot of the dataset
##################################
xray_images.head()

	Image_ID	Path	Diagnosis	Target	Class	Image
0	COVID-1	C:/Users/John pauline magno/Python Notebooks/C...	COVID	0	Covid-19	[[15, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
1	COVID-10	C:/Users/John pauline magno/Python Notebooks/C...	COVID	0	Covid-19	[[129, 125, 123, 121, 119, 117, 114, 104, 104,...
2	COVID-100	C:/Users/John pauline magno/Python Notebooks/C...	COVID	0	Covid-19	[[11, 0, 0, 3, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0...
3	COVID-1000	C:/Users/John pauline magno/Python Notebooks/C...	COVID	0	Covid-19	[[42, 39, 38, 42, 38, 35, 31, 26, 24, 24, 24, ...
4	COVID-1001	C:/Users/John pauline magno/Python Notebooks/C...	COVID	0	Covid-19	[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 1, 0,...

##################################
# Taking a snapshot of the dataset
##################################
n_samples = 5
fig, m_axs = plt.subplots(3, n_samples, figsize = (3*n_samples, 8))
for n_axs, (type_name, type_rows) in zip(m_axs, xray_images.sort_values(['Diagnosis']).groupby('Diagnosis')):
    n_axs[2].set_title(type_name, fontsize = 14, weight = 'bold')
    for c_ax, (_, c_row) in zip(n_axs, type_rows.sample(n_samples, random_state=1).iterrows()):       
        picture = c_row['Path']
        image = cv2.imread(picture)
        c_ax.imshow(image)
        c_ax.axis('off')

##################################
# Sampling a single image
##################################
samples, features = xray_images.shape
plt.figure()
pic_id = random.randrange(0, samples)
picture = xray_images['Path'][pic_id]
image = cv2.imread(picture) 

<Figure size 640x480 with 0 Axes>

##################################
# Plotting using subplots
##################################
plt.figure(figsize=(15, 5))

##################################
# Formulating the original image
##################################
plt.subplot(1, 4, 1)
plt.imshow(image)
plt.title('Original Image', fontsize = 14, weight = 'bold')
plt.axis('off')

##################################
# Formulating the blue channel
##################################
plt.subplot(1, 4, 2)
plt.imshow(image[ : , : , 0])
plt.title('Blue Channel', fontsize = 14, weight = 'bold')
plt.axis('off')

##################################
# Formulating the green channel
##################################
plt.subplot(1, 4, 3)
plt.imshow(image[ : , : , 1])
plt.title('Green Channel', fontsize = 14, weight = 'bold')
plt.axis('off')

##################################
# Formulating the red channel
##################################
plt.subplot(1, 4, 4)
plt.imshow(image[ : , : , 2])
plt.title('Red Channel', fontsize = 14, weight = 'bold')
plt.axis('off')

##################################
# Consolidating all images
##################################
plt.show()

##################################
# Determining the image shape
##################################
print('Image Shape:')
display(image.shape)

Image Shape:

(299, 299, 3)

##################################
# Determining the image height
##################################
print('Image Height:')
display(image.shape[0])

Image Height:

299

##################################
# Determining the image width
##################################
print('Image Width:')
display(image.shape[0])

Image Width:

299

##################################
# Determining the image dimension
##################################
print('Image Dimension:')
display(image.ndim)

Image Dimension:

3

##################################
# Determining the image size
##################################
print('Image Size:')
display(image.size)

Image Size:

268203

##################################
# Determining the image data type
##################################
print('Image Data Type:')
display(image.dtype)

Image Data Type:

dtype('uint8')

##################################
# Determining the maximum RGB value
##################################
print('Image Maximum RGB:')
display(image.max())

Image Maximum RGB:

205

##################################
# Determining the minimum RGB value
##################################
print('Image Minimum RGB:')
display(image.min())

Image Minimum RGB:

10

1.4.2 Image Augmentation

1. Details
   • 1.1 Details
     • 1.1.1 Details
       • 1.1.1.1 Details

##################################
# Identifying the path for the images
# and defining image categories 
##################################
path = 'C:/Users/John pauline magno/Python Notebooks/COVID-19_Radiography_Dataset'
classes=["COVID", "Normal", "Viral Pneumonia"]
num_classes = len(classes)
batch_size = 16

##################################
# Creating subsets of images
# for model training and
# setting the parameters for
# real-time data augmentation
# at each epoch
##################################
set_seed()
train_datagen = ImageDataGenerator(rescale=1./255,
                                   rotation_range=20,
                                   width_shift_range=0.2,
                                   height_shift_range=0.2,
                                   horizontal_flip=True,
                                   validation_split=0.2)


##################################
# Loading the model training images
##################################
train_gen = train_datagen.flow_from_directory(directory=path, 
                                              target_size=(299, 299),
                                              class_mode='categorical',
                                              subset='training',
                                              shuffle=True, 
                                              classes=classes,
                                              batch_size=batch_size, 
                                              color_mode="grayscale")

Found 2880 images belonging to 3 classes.

##################################
# Loading samples of augmented images
# for the training set
##################################
fig, axes = plt.subplots(1, 5, figsize=(15, 3))

for i in range(5):
    batch = next(train_gen)
    images, labels = batch
    axes[i].imshow(images[0])  # Display the first image in the batch
    axes[i].set_title(f"Label: {labels[0]}")
    axes[i].axis('off')
plt.show()

##################################
# Creating subsets of images
# for model validation
# setting the parameters for
# real-time data augmentation
# at each epoch
##################################
set_seed()
test_datagen = ImageDataGenerator(rescale=1./255, 
                                  validation_split=0.2)

##################################
# Loading the model evaluation images
##################################
test_gen = test_datagen.flow_from_directory(directory=path, 
                                              target_size=(299, 299),
                                              class_mode='categorical',
                                              subset='validation',
                                              shuffle=False, classes=classes,
                                              batch_size=batch_size, 
                                              color_mode="grayscale")

Found 720 images belonging to 3 classes.

##################################
# Loading samples of augmented images
# for the validation set
##################################
fig, axes = plt.subplots(1, 5, figsize=(15, 3))

for i in range(5):
    batch = next(test_gen)
    images, labels = batch
    axes[i].imshow(images[0])
    axes[i].set_title(f"Label: {labels[0]}")
    axes[i].axis('off')
plt.show()

1.5. Data Exploration

1.5.1 Exploratory Data Analysis

1. Details
   • 1.1 Details
     • 1.1.1 Details
       • 1.1.1.1 Details

##################################
# Consolidating summary statistics
# for the image pixel values
##################################
mean_val = []
std_dev_val = []
max_val = []
min_val = []

for i in range(0, samples):
    mean_val.append(xray_images['Image'][i].mean())
    std_dev_val.append(np.std(xray_images['Image'][i]))
    max_val.append(xray_images['Image'][i].max())
    min_val.append(xray_images['Image'][i].min())

imageEDA = xray_images.loc[:,['Image', 'Class','Path']]
imageEDA['Mean'] = mean_val
imageEDA['StDev'] = std_dev_val
imageEDA['Max'] = max_val
imageEDA['Min'] = min_val

##################################
# Formulating the mean distribution
# by category of the image pixel values
##################################
sns.displot(data = imageEDA, x = 'Mean', kind="kde", hue = 'Class', height=6, aspect=1.40)
plt.title('Image Pixel Mean Distribution by Category', fontsize=14, weight='bold');

##################################
# Formulating the maximum distribution
# by category of the image pixel values
##################################
sns.displot(data = imageEDA, x = 'Max', kind="kde", hue = 'Class', height=6, aspect=1.40)
plt.title('Image Pixel Maximum Distribution by Category', fontsize=14, weight='bold');

##################################
# Formulating the minimum distribution
# by category of the image pixel values
##################################
sns.displot(data = imageEDA, x = 'Min', kind="kde", hue = 'Class', height=6, aspect=1.40)
plt.title('Image Pixel Minimum Distribution by Category', fontsize=14, weight='bold');

##################################
# Formulating the standard deviation distribution
# by category of the image pixel values
##################################
sns.displot(data = imageEDA, x = 'StDev', kind="kde", hue = 'Class', height=6, aspect=1.40)
plt.title('Image Pixel Standard Deviation Distribution by Category', fontsize=14, weight='bold');

##################################
# Formulating the mean and standard deviation 
# scatterplot distribution
# by category of the image pixel values
##################################
plt.figure(figsize=(10,6))
sns.set(style="ticks", font_scale = 1)
ax = sns.scatterplot(data=imageEDA, x="Mean", y=imageEDA['StDev'], hue='Class', alpha=0.5)
sns.despine(top=True, right=True, left=False, bottom=False)
plt.xticks(rotation=0, fontsize = 12)
ax.set_xlabel('Image Pixel Mean',fontsize=14, weight='bold')
ax.set_ylabel('Image Pixel Standard Deviation', fontsize=14, weight='bold')
plt.title('Image Pixel Mean and Standard Deviation Distribution', fontsize = 14, weight='bold');

##################################
# Formulating the mean and standard deviation 
# scatterplot distribution
# by category of the image pixel values
##################################
scatterplot = sns.FacetGrid(imageEDA, col="Class", height=6)
scatterplot.map_dataframe(sns.scatterplot, x='Mean', y='StDev', alpha=0.5)
scatterplot.set_titles(col_template="{col_name}", row_template="{row_name}", size=18)
scatterplot.fig.subplots_adjust(top=.8)
scatterplot.fig.suptitle('Image Pixel Mean and Standard Deviation Distribution', fontsize=14, weight='bold')
axes = scatterplot.axes.flatten()
axes[0].set_ylabel('Image Pixel Standard Deviation')
for ax in axes:
    ax.set_xlabel('Image Pixel Mean')
scatterplot.fig.tight_layout()

##################################
# Formulating the mean and standard deviation 
# scatterplot distribution
# of the image pixel values
# represented as actual images
##################################
def getImage(path):
    return OffsetImage(cv2.imread(path),zoom = 0.1)

DF_sample = imageEDA.sample(frac=1.0, replace=False, random_state=1)
paths = DF_sample['Path']

fig, ax = plt.subplots(figsize=(20,8))
ab = sns.scatterplot(data=DF_sample, x="Mean", y='StDev')
sns.despine(top=True, right=True, left=False, bottom=False)
ax.set_xlabel('Image Pixel Mean', fontsize=14, weight='bold')
ax.set_ylabel('Image Pixel Standard Deviation', fontsize=14, weight='bold')
plt.title('Image Pixel Mean and Standard Deviation Distribution', fontsize=14, weight='bold');

for x0, y0, path in zip(DF_sample['Mean'], DF_sample['StDev'],paths):
    ab = AnnotationBbox(getImage(path), (x0, y0), frameon=False)
    ax.add_artist(ab)

1.6. Model Development

1.6.1 Premodelling Data Description

1. Details
   • 1.1 Details
     • 1.1.1 Details
       • 1.1.1.1 Details

1.6.2 CNN With No Regularization

1. Details
   • 1.1 Details
     • 1.1.1 Details
       • 1.1.1.1 Details

##################################
# Defining a function for
# plotting the loss profile
# of the training and validation sets
#################################
def plot_training_history(history, model_name):
    plt.figure(figsize=(10,6))
    plt.plot(history.history['loss'], label='Train')
    plt.plot(history.history['val_loss'], label='Validation')
    plt.title(f'{model_name} Training Loss', fontsize = 16, weight = 'bold', pad=20)
    plt.ylim(0, 5)
    plt.xlabel('Epoch', fontsize = 14, weight = 'bold',)
    plt.ylabel('Loss', fontsize = 14, weight = 'bold',)
    plt.legend()
    plt.show()

##################################
# Formulating the network architecture
# for CNN with no regularization
##################################
set_seed()
batch_size = 16
model_nr = Sequential()
model_nr.add(Conv2D(32, kernel_size=(3, 3), activation='relu', padding = 'Same', input_shape=(299, 299, 1)))
model_nr.add(MaxPooling2D(pool_size=(2, 2)))
model_nr.add(Conv2D(64, kernel_size=(3, 3), padding = 'Same', activation='relu'))
model_nr.add(MaxPooling2D(pool_size=(2, 2)))
model_nr.add(Flatten())
model_nr.add(Dense(128, activation='relu'))
model_nr.add(Dense(num_classes, activation='softmax'))

##################################
# Compiling the network layers
##################################
model_nr.compile(loss='categorical_crossentropy', optimizer='adam', metrics=[Recall()])

WARNING:tensorflow:From C:\Users\John pauline magno\AppData\Roaming\Python\Python311\site-packages\keras\backend.py:873: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.

WARNING:tensorflow:From C:\Users\John pauline magno\AppData\Roaming\Python\Python311\site-packages\keras\layers\pooling\max_pooling2d.py:160: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.

WARNING:tensorflow:From C:\Users\John pauline magno\AppData\Roaming\Python\Python311\site-packages\keras\optimizers\__init__.py:300: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.

##################################
# Displaying the model summary
# for CNN with no regularization
##################################
print(model_nr.summary())

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 299, 299, 32)      320       
                                                                 
 max_pooling2d (MaxPooling2D  (None, 149, 149, 32)     0         
 )                                                               
                                                                 
 conv2d_1 (Conv2D)           (None, 149, 149, 64)      18496     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 74, 74, 64)       0         
 2D)                                                             
                                                                 
 flatten (Flatten)           (None, 350464)            0         
                                                                 
 dense (Dense)               (None, 128)               44859520  
                                                                 
 dense_1 (Dense)             (None, 3)                 387       
                                                                 
=================================================================
Total params: 44,878,723
Trainable params: 44,878,723
Non-trainable params: 0
_________________________________________________________________
None

##################################
# Displaying the model layers
# for CNN with no regularization
##################################
model_nr_layer_names = [layer.name for layer in model_nr.layers]
print("Layer Names:", model_nr_layer_names)

Layer Names: ['conv2d', 'max_pooling2d', 'conv2d_1', 'max_pooling2d_1', 'flatten', 'dense', 'dense_1']

##################################
# Displaying the number of weights
# for each model layer
# for CNN with no regularization
##################################
for layer in model_nr.layers:
    if hasattr(layer, 'weights'):
        print(f"Layer: {layer.name}, Number of Weights: {len(layer.get_weights())}")

Layer: conv2d, Number of Weights: 2
Layer: max_pooling2d, Number of Weights: 0
Layer: conv2d_1, Number of Weights: 2
Layer: max_pooling2d_1, Number of Weights: 0
Layer: flatten, Number of Weights: 0
Layer: dense, Number of Weights: 2
Layer: dense_1, Number of Weights: 2

##################################
# Displaying the number of weights
# for each model layer
# for CNN with no regularization
##################################
total_parameters = 0
for layer in model_nr.layers:
    layer_parameters = layer.count_params()
    total_parameters += layer_parameters
    print(f"Layer: {layer.name}, Parameters: {layer_parameters}")
print("\nTotal Parameters in the Model:", total_parameters)

Layer: conv2d, Parameters: 320
Layer: max_pooling2d, Parameters: 0
Layer: conv2d_1, Parameters: 18496
Layer: max_pooling2d_1, Parameters: 0
Layer: flatten, Parameters: 0
Layer: dense, Parameters: 44859520
Layer: dense_1, Parameters: 387

Total Parameters in the Model: 44878723

##################################
# Fitting the model
# for CNN with no regularization
##################################
epochs = 100
set_seed()
model_nr_history = model_nr.fit(train_gen, 
                                steps_per_epoch=len(train_gen) // batch_size,   
                                validation_steps=len(test_gen) // batch_size, 
                                validation_data=test_gen, 
                                epochs=epochs,
                                verbose=0)

WARNING:tensorflow:From C:\Users\John pauline magno\AppData\Roaming\Python\Python311\site-packages\keras\utils\tf_utils.py:490: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.

##################################
# Evaluating the model
# for CNN with no regularization
# on the independent validation set
##################################
model_nr_y_pred = model_nr.predict(test_gen)

45/45 [==============================] - 4s 81ms/step

##################################
# Plotting the loss profile
# for CNN with no regularization
# on the training and validation sets
##################################
plot_training_history(model_nr_history, 'CNN With No Regularization : ')

##################################
# Consolidating the predictions
# for CNN with no regularization
# on the validation set
##################################
model_nr_predictions = np.array(list(map(lambda x: np.argmax(x), model_nr_y_pred)))
model_nr_y_true = test_gen.classes

##################################
# Formulating the confusion matrix
# for CNN with no regularization
# on the validation set
##################################
CMatrix = pd.DataFrame(confusion_matrix(model_nr_y_true, model_nr_predictions), columns=classes, index =classes)

##################################
# Plotting the confusion matrix
# for CNN with no regularization
# on the validation set
##################################
plt.figure(figsize=(10, 6))
ax = sns.heatmap(CMatrix, annot = True, fmt = 'g' ,vmin = 0, vmax = 250,cmap = 'icefire')
ax.set_xlabel('Predicted',fontsize = 14,weight = 'bold')
ax.set_xticklabels(ax.get_xticklabels(),rotation =0)
ax.set_ylabel('Actual',fontsize = 14,weight = 'bold') 
ax.set_yticklabels(ax.get_yticklabels(),rotation =0)
ax.set_title('CNN With No Regularization : Validation Set Confusion Matrix',fontsize = 14, weight = 'bold',pad=20);

##################################
# Resetting all states generated by Keras
##################################
keras.backend.clear_session()

##################################
# Calculating the model accuracy
# for CNN with no regularization
# for the entire validation set
##################################
model_nr_acc = accuracy_score(model_nr_y_true, model_nr_predictions)

##################################
# Calculating the model 
# Precision, Recall, F-score and Support
# for CNN with no regularization
# for the entire validation set
##################################
model_nr_results_all = precision_recall_fscore_support(model_nr_y_true, model_nr_predictions, average='macro',zero_division = 1)

##################################
# Calculating the model 
# Precision, Recall, F-score and Support
# for CNN with no regularization
# for each category of the validation set
##################################
model_nr_results_class = precision_recall_fscore_support(model_nr_y_true, model_nr_predictions, average=None, zero_division = 1)

##################################
# Consolidating all model evaluation metrics 
# for CNN with no regularization
##################################
metric_columns = ['Precision','Recall','F-Score','Support']
model_nr_all_df = pd.concat([pd.DataFrame(list(model_nr_results_class)).T,pd.DataFrame(list(model_nr_results_all)).T])
model_nr_all_df.columns = metric_columns
model_nr_all_df.index = ['COVID', 'Normal', 'Viral Pneumonia','Total']
model_nr_all_df

	Precision	Recall	F-Score	Support
COVID	0.954545	0.875000	0.913043	240.0
Normal	0.909091	0.875000	0.891720	240.0
Viral Pneumonia	0.825279	0.925000	0.872299	240.0
Total	0.896305	0.891667	0.892354	NaN

##################################
# Consolidating all model evaluation metrics 
# for CNN with no regularization
##################################
model_nr_model_list = []
model_nr_measure_list = []
model_nr_category_list = []
model_nr_value_list = []

for i in range(3): 
    for j in range(4):
        model_nr_model_list.append('CNN_NR')
        model_nr_measure_list.append(metric_columns[i])
        model_nr_category_list.append(model_nr_all_df.index[j])
        model_nr_value_list.append(model_nr_all_df.iloc[j,i])

model_nr_all_summary = pd.DataFrame(zip(model_nr_model_list,
                                        model_nr_measure_list,
                                        model_nr_category_list,
                                        model_nr_value_list), 
                                        columns=['CNN.Model.Name',
                                                 'Model.Metric',
                                                 'Image.Category',
                                                 'Metric.Value'])

1.6.3 CNN With Dropout Regularization

1. Details
   • 1.1 Details
     • 1.1.1 Details
       • 1.1.1.1 Details

##################################
# Formulating the network architecture
# for CNN with dropout regularization
##################################
set_seed()
batch_size = 16
model_dr = Sequential()
model_dr.add(Conv2D(32, kernel_size=(3, 3), activation='relu', padding = 'Same', input_shape=(299, 299, 1)))
model_dr.add(MaxPooling2D(pool_size=(2, 2)))
model_dr.add(Conv2D(64, kernel_size=(3, 3), padding = 'Same', activation='relu'))
model_dr.add(Dropout(0.25))
model_dr.add(MaxPooling2D(pool_size=(2, 2)))
model_dr.add(Flatten())
model_dr.add(Dense(128, activation='relu'))
model_dr.add(Dense(num_classes, activation='softmax'))

##################################
# Compiling the network layers
##################################
model_dr.compile(loss='categorical_crossentropy', optimizer='adam', metrics=[Recall()])

##################################
# Displaying the model summary
# for CNN with dropout regularization
##################################
print(model_dr.summary())

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 299, 299, 32)      320       
                                                                 
 max_pooling2d (MaxPooling2D  (None, 149, 149, 32)     0         
 )                                                               
                                                                 
 conv2d_1 (Conv2D)           (None, 149, 149, 64)      18496     
                                                                 
 dropout (Dropout)           (None, 149, 149, 64)      0         
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 74, 74, 64)       0         
 2D)                                                             
                                                                 
 flatten (Flatten)           (None, 350464)            0         
                                                                 
 dense (Dense)               (None, 128)               44859520  
                                                                 
 dense_1 (Dense)             (None, 3)                 387       
                                                                 
=================================================================
Total params: 44,878,723
Trainable params: 44,878,723
Non-trainable params: 0
_________________________________________________________________
None

##################################
# Displaying the model layers
# for CNN with dropout regularization
##################################
model_dr_layer_names = [layer.name for layer in model_dr.layers]
print("Layer Names:", model_dr_layer_names)

Layer Names: ['conv2d', 'max_pooling2d', 'conv2d_1', 'dropout', 'max_pooling2d_1', 'flatten', 'dense', 'dense_1']

##################################
# Displaying the number of weights
# for each model layer
# for CNN with dropout regularization
##################################
for layer in model_dr.layers:
    if hasattr(layer, 'weights'):
        print(f"Layer: {layer.name}, Number of Weights: {len(layer.get_weights())}")

Layer: conv2d, Number of Weights: 2
Layer: max_pooling2d, Number of Weights: 0
Layer: conv2d_1, Number of Weights: 2
Layer: dropout, Number of Weights: 0
Layer: max_pooling2d_1, Number of Weights: 0
Layer: flatten, Number of Weights: 0
Layer: dense, Number of Weights: 2
Layer: dense_1, Number of Weights: 2

##################################
# Displaying the number of weights
# for each model layer
# for CNN with dropout regularization
##################################
total_parameters = 0
for layer in model_dr.layers:
    layer_parameters = layer.count_params()
    total_parameters += layer_parameters
    print(f"Layer: {layer.name}, Parameters: {layer_parameters}")
print("\nTotal Parameters in the Model:", total_parameters)

Layer: conv2d, Parameters: 320
Layer: max_pooling2d, Parameters: 0
Layer: conv2d_1, Parameters: 18496
Layer: dropout, Parameters: 0
Layer: max_pooling2d_1, Parameters: 0
Layer: flatten, Parameters: 0
Layer: dense, Parameters: 44859520
Layer: dense_1, Parameters: 387

Total Parameters in the Model: 44878723

##################################
# Fitting the model
# for CNN with dropout regularization
##################################
epochs = 100
set_seed()
model_dr_history = model_dr.fit(train_gen, 
                                steps_per_epoch=len(train_gen) // batch_size, 
                                validation_steps=len(test_gen) // batch_size, 
                                validation_data=test_gen, 
                                epochs=epochs,
                                verbose=0)

##################################
# Evaluating the model
# for CNN with dropout regularization
# on the independent validation set
##################################
model_dr_y_pred = model_dr.predict(test_gen)

45/45 [==============================] - 3s 71ms/step

##################################
# Plotting the loss profile
# for CNN with dropout regularization
# on the training and validation sets
##################################
plot_training_history(model_dr_history, 'CNN With Dropout Regularization : ')

##################################
# Consolidating the predictions
# for CNN with dropout regularization
# on the validation set
##################################
model_dr_predictions = np.array(list(map(lambda x: np.argmax(x), model_dr_y_pred)))
model_dr_y_true=test_gen.classes

##################################
# Formulating the confusion matrix
# for CNN with dropout regularization
# on the validation set
##################################
CMatrix = pd.DataFrame(confusion_matrix(model_dr_y_true, model_dr_predictions), columns=classes, index =classes)

##################################
# Calculating the model 
# Precision, Recall, F-score and Support
# for CNN with dropout regularization
# for each category of the validation set
##################################
plt.figure(figsize=(10, 6))
ax = sns.heatmap(CMatrix, annot = True, fmt = 'g' ,vmin = 0, vmax = 250, cmap = 'icefire')
ax.set_xlabel('Predicted',fontsize = 14,weight = 'bold')
ax.set_xticklabels(ax.get_xticklabels(),rotation =0)
ax.set_ylabel('Actual',fontsize = 14,weight = 'bold') 
ax.set_yticklabels(ax.get_yticklabels(),rotation =0)
ax.set_title('CNN With Dropout Regularization : Validation Set Confusion Matrix',fontsize = 14, weight = 'bold', pad=20);

##################################
# Resetting all states generated by Keras
##################################
keras.backend.clear_session()

##################################
# Calculating the model accuracy
# for CNN with dropout regularization
# for the entire validation set
##################################
model_dr_acc = accuracy_score(model_dr_y_true, model_dr_predictions)

##################################
# Calculating the model 
# Precision, Recall, F-score and Support
# for CNN with dropout regularization
# for the entire validation set
##################################
model_dr_results_all = precision_recall_fscore_support(model_dr_y_true, model_dr_predictions, average='macro',zero_division = 1)

##################################
# Calculating the model 
# Precision, Recall, F-score and Support
# for CNN with dropout regularization
# for each category of the validation set
##################################
model_dr_results_class = precision_recall_fscore_support(model_dr_y_true, model_dr_predictions, average=None, zero_division = 1)

##################################
# Consolidating all model evaluation metrics 
# for CNN with dropout regularization
##################################
metric_columns = ['Precision','Recall', 'F-Score','Support']
model_dr_all_df = pd.concat([pd.DataFrame(list(model_dr_results_class)).T,pd.DataFrame(list(model_dr_results_all)).T])
model_dr_all_df.columns = metric_columns
model_dr_all_df.index = ['COVID', 'Normal', 'Viral Pneumonia','Total']
model_dr_all_df

	Precision	Recall	F-Score	Support
COVID	0.962791	0.862500	0.909890	240.0
Normal	0.847015	0.945833	0.893701	240.0
Viral Pneumonia	0.881857	0.870833	0.876310	240.0
Total	0.897221	0.893056	0.893300	NaN

##################################
# Consolidating all model evaluation metrics 
# for CNN with dropout regularization
##################################
model_dr_model_list = []
model_dr_measure_list = []
model_dr_category_list = []
model_dr_value_list = []

for i in range(3): 
    for j in range(4):
        model_dr_model_list.append('CNN_DR')
        model_dr_measure_list.append(metric_columns[i])
        model_dr_category_list.append(model_dr_all_df.index[j])
        model_dr_value_list.append(model_dr_all_df.iloc[j,i])

model_dr_all_summary = pd.DataFrame(zip(model_dr_model_list,
                                        model_dr_measure_list,
                                        model_dr_category_list,
                                        model_dr_value_list), 
                                        columns=['CNN.Model.Name',
                                                 'Model.Metric',
                                                 'Image.Category',
                                                 'Metric.Value'])

1.6.4 CNN With Batch Normalization Regularization

1. Details
   • 1.1 Details
     • 1.1.1 Details
       • 1.1.1.1 Details

##################################
# Formulating the network architecture
# for CNN with batch normalization regularization
##################################
set_seed()
batch_size = 16
model_bnr = Sequential()
model_bnr.add(Conv2D(32, kernel_size=(3, 3), activation='relu', padding = 'Same', input_shape=(299, 299, 1)))
model_bnr.add(MaxPooling2D(pool_size=(2, 2)))
model_bnr.add(Conv2D(64, kernel_size=(3, 3), padding = 'Same', activation='relu'))
model_bnr.add(BatchNormalization())
model_bnr.add(Activation('relu'))
model_bnr.add(MaxPooling2D(pool_size=(2, 2)))
model_bnr.add(Flatten())
model_bnr.add(Dense(128, activation='relu'))
model_bnr.add(Dense(num_classes, activation='softmax'))

##################################
# Compiling the network layers
##################################
model_bnr.compile(loss='categorical_crossentropy', optimizer='adam', metrics=[Recall()])

##################################
# Displaying the model summary
# for CNN with batch normalization regularization
##################################
print(model_bnr.summary())

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 299, 299, 32)      320       
                                                                 
 max_pooling2d (MaxPooling2D  (None, 149, 149, 32)     0         
 )                                                               
                                                                 
 conv2d_1 (Conv2D)           (None, 149, 149, 64)      18496     
                                                                 
 batch_normalization (BatchN  (None, 149, 149, 64)     256       
 ormalization)                                                   
                                                                 
 activation (Activation)     (None, 149, 149, 64)      0         
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 74, 74, 64)       0         
 2D)                                                             
                                                                 
 flatten (Flatten)           (None, 350464)            0         
                                                                 
 dense (Dense)               (None, 128)               44859520  
                                                                 
 dense_1 (Dense)             (None, 3)                 387       
                                                                 
=================================================================
Total params: 44,878,979
Trainable params: 44,878,851
Non-trainable params: 128
_________________________________________________________________
None

##################################
# Displaying the model layers
# for CNN with batch normalization regularization
##################################
model_bnr_layer_names = [layer.name for layer in model_bnr.layers]
print("Layer Names:", model_bnr_layer_names)

Layer Names: ['conv2d', 'max_pooling2d', 'conv2d_1', 'batch_normalization', 'activation', 'max_pooling2d_1', 'flatten', 'dense', 'dense_1']

##################################
# Displaying the number of weights
# for each model layer
# for CNN with batch normalization regularization
##################################
for layer in model_bnr.layers:
    if hasattr(layer, 'weights'):
        print(f"Layer: {layer.name}, Number of Weights: {len(layer.get_weights())}")

Layer: conv2d, Number of Weights: 2
Layer: max_pooling2d, Number of Weights: 0
Layer: conv2d_1, Number of Weights: 2
Layer: batch_normalization, Number of Weights: 4
Layer: activation, Number of Weights: 0
Layer: max_pooling2d_1, Number of Weights: 0
Layer: flatten, Number of Weights: 0
Layer: dense, Number of Weights: 2
Layer: dense_1, Number of Weights: 2

##################################
# Displaying the number of weights
# for each model layer
# for CNN with batch normalization regularization
##################################
total_parameters = 0
for layer in model_bnr.layers:
    layer_parameters = layer.count_params()
    total_parameters += layer_parameters
    print(f"Layer: {layer.name}, Parameters: {layer_parameters}")
print("\nTotal Parameters in the Model:", total_parameters)

Layer: conv2d, Parameters: 320
Layer: max_pooling2d, Parameters: 0
Layer: conv2d_1, Parameters: 18496
Layer: batch_normalization, Parameters: 256
Layer: activation, Parameters: 0
Layer: max_pooling2d_1, Parameters: 0
Layer: flatten, Parameters: 0
Layer: dense, Parameters: 44859520
Layer: dense_1, Parameters: 387

Total Parameters in the Model: 44878979

##################################
# Fitting the model
# for CNN with batch normalization regularization
##################################
epochs = 100
set_seed()
model_bnr_history = model_bnr.fit(train_gen, 
                                  steps_per_epoch=len(train_gen) // batch_size,
                                  validation_steps=len(test_gen) // batch_size, 
                                  validation_data=test_gen, epochs=epochs,
                                  verbose=0)

##################################
# Evaluating the model
# for CNN with batch normalization regularization
# on the independent validation set
##################################
model_bnr_y_pred = model_bnr.predict(test_gen)

45/45 [==============================] - 4s 79ms/step

##################################
# Plotting the loss profile
# for CNN with batch normalization regularization
# on the training and validation sets
##################################
plot_training_history(model_bnr_history, 'CNN With Batch Normalization Regularization : ')

##################################
# Consolidating the predictions
# for CNN with batch normalization regularization
# on the validation set
##################################
model_bnr_predictions = np.array(list(map(lambda x: np.argmax(x), model_bnr_y_pred)))
model_bnr_y_true = test_gen.classes

##################################
# Formulating the confusion matrix
# for CNN with batch normalization regularization
# on the validation set
##################################
CMatrix = pd.DataFrame(confusion_matrix(model_bnr_y_true, model_bnr_predictions), columns=classes, index =classes)

##################################
# Calculating the model 
# Precision, Recall, F-score and Support
# for CNN with batch normalization regularization
# for each category of the validation set
##################################
plt.figure(figsize=(10, 6))
ax = sns.heatmap(CMatrix, annot = True, fmt = 'g' ,vmin = 0, vmax = 250,cmap = 'icefire')
ax.set_xlabel('Predicted',fontsize = 14,weight = 'bold')
ax.set_xticklabels(ax.get_xticklabels(),rotation =0)
ax.set_ylabel('Actual',fontsize = 14,weight = 'bold') 
ax.set_yticklabels(ax.get_yticklabels(),rotation =0)
ax.set_title('CNN With Batch Normalization Regularization : Validation Set Confusion Matrix',fontsize = 16,weight = 'bold',pad=20);

##################################
# Resetting all states generated by Keras
##################################
keras.backend.clear_session()

##################################
# Calculating the model accuracy
# for CNN with batch normalization regularization
# for the entire validation set
##################################
model_bnr_acc = accuracy_score(model_bnr_y_true, model_bnr_predictions)

##################################
# Calculating the model 
# Precision, Recall, F-score and Support
# for CNN with batch normalization regularization
# for the entire validation set
##################################
model_bnr_results_all = precision_recall_fscore_support(model_bnr_y_true, model_bnr_predictions, average='macro',zero_division = 1)

##################################
# Calculating the model 
# Precision, Recall, F-score and Support
# for CNN with batch normalization regularization
# for each category of the validation set
##################################
model_bnr_results_class = precision_recall_fscore_support(model_bnr_y_true, model_bnr_predictions, average=None, zero_division = 1)

##################################
# Consolidating all model evaluation metrics 
# for CNN with batch normalization regularization
##################################
metric_columns = ['Precision','Recall', 'F-Score','Support']
model_bnr_all_df = pd.concat([pd.DataFrame(list(model_bnr_results_class)).T,pd.DataFrame(list(model_bnr_results_all)).T])
model_bnr_all_df.columns = metric_columns
model_bnr_all_df.index = ['COVID', 'Normal', 'Viral Pneumonia','Total']
model_bnr_all_df

	Precision	Recall	F-Score	Support
COVID	0.924686	0.920833	0.922756	240.0
Normal	0.837302	0.879167	0.857724	240.0
Viral Pneumonia	0.877729	0.837500	0.857143	240.0
Total	0.879906	0.879167	0.879207	NaN

##################################
# Consolidating all model evaluation metrics 
# for CNN with batch normalization regularization
##################################
model_bnr_model_list = []
model_bnr_measure_list = []
model_bnr_category_list = []
model_bnr_value_list = []

for i in range(3): 
    for j in range(4):
        model_bnr_model_list.append('CNN_BNR')
        model_bnr_measure_list.append(metric_columns[i])
        model_bnr_category_list.append(model_bnr_all_df.index[j])
        model_bnr_value_list.append(model_bnr_all_df.iloc[j,i])

model_bnr_all_summary = pd.DataFrame(zip(model_bnr_model_list,
                                        model_bnr_measure_list,
                                        model_bnr_category_list,
                                        model_bnr_value_list), 
                                        columns=['CNN.Model.Name',
                                                 'Model.Metric',
                                                 'Image.Category',
                                                 'Metric.Value'])

1.6.5 CNN With Dropout and Batch Normalization Regularization

1. Details
   • 1.1 Details
     • 1.1.1 Details
       • 1.1.1.1 Details

##################################
# Formulating the network architecture
# for CNN with dropout and batch normalization regularization
##################################
set_seed()
batch_size = 16
model_dr_bnr = Sequential()
model_dr_bnr.add(Conv2D(32, kernel_size=(3, 3), activation='relu', padding = 'Same', input_shape=(299, 299, 1)))
model_dr_bnr.add(MaxPooling2D(pool_size=(2, 2)))
model_dr_bnr.add(Conv2D(64, kernel_size=(3, 3), padding = 'Same', activation='relu'))
model_dr_bnr.add(BatchNormalization())
model_dr_bnr.add(Activation('relu'))
model_dr_bnr.add(Dropout(0.25))
model_dr_bnr.add(MaxPooling2D(pool_size=(2, 2)))
model_dr_bnr.add(Flatten())
model_dr_bnr.add(Dense(128, activation='relu'))
model_dr_bnr.add(Dense(num_classes, activation='softmax'))

##################################
# Compiling the network layers
##################################
model_dr_bnr.compile(loss='categorical_crossentropy', optimizer='adam', metrics=[Recall()])

##################################
# Displaying the model summary
# for CNN with dropout and
# batch normalization regularization
##################################
print(model_dr_bnr.summary())

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 299, 299, 32)      320       
                                                                 
 max_pooling2d (MaxPooling2D  (None, 149, 149, 32)     0         
 )                                                               
                                                                 
 conv2d_1 (Conv2D)           (None, 149, 149, 64)      18496     
                                                                 
 batch_normalization (BatchN  (None, 149, 149, 64)     256       
 ormalization)                                                   
                                                                 
 activation (Activation)     (None, 149, 149, 64)      0         
                                                                 
 dropout (Dropout)           (None, 149, 149, 64)      0         
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 74, 74, 64)       0         
 2D)                                                             
                                                                 
 flatten (Flatten)           (None, 350464)            0         
                                                                 
 dense (Dense)               (None, 128)               44859520  
                                                                 
 dense_1 (Dense)             (None, 3)                 387       
                                                                 
=================================================================
Total params: 44,878,979
Trainable params: 44,878,851
Non-trainable params: 128
_________________________________________________________________
None

##################################
# Displaying the model layers
# for CNN with dropout and
# batch normalization regularization
##################################
model_dr_bnr_layer_names = [layer.name for layer in model_dr_bnr.layers]
print("Layer Names:", model_dr_bnr_layer_names)

Layer Names: ['conv2d', 'max_pooling2d', 'conv2d_1', 'batch_normalization', 'activation', 'dropout', 'max_pooling2d_1', 'flatten', 'dense', 'dense_1']

##################################
# Displaying the number of weights
# for CNN with dropout and
# batch normalization regularization
##################################
for layer in model_dr_bnr.layers:
    if hasattr(layer, 'weights'):
        print(f"Layer: {layer.name}, Number of Weights: {len(layer.get_weights())}")

Layer: conv2d, Number of Weights: 2
Layer: max_pooling2d, Number of Weights: 0
Layer: conv2d_1, Number of Weights: 2
Layer: batch_normalization, Number of Weights: 4
Layer: activation, Number of Weights: 0
Layer: dropout, Number of Weights: 0
Layer: max_pooling2d_1, Number of Weights: 0
Layer: flatten, Number of Weights: 0
Layer: dense, Number of Weights: 2
Layer: dense_1, Number of Weights: 2

##################################
# Displaying the number of weights
# for CNN with dropout and
# batch normalization regularization
##################################
total_parameters = 0
for layer in model_dr_bnr.layers:
    layer_parameters = layer.count_params()
    total_parameters += layer_parameters
    print(f"Layer: {layer.name}, Parameters: {layer_parameters}")
print("\nTotal Parameters in the Model:", total_parameters)

Layer: conv2d, Parameters: 320
Layer: max_pooling2d, Parameters: 0
Layer: conv2d_1, Parameters: 18496
Layer: batch_normalization, Parameters: 256
Layer: activation, Parameters: 0
Layer: dropout, Parameters: 0
Layer: max_pooling2d_1, Parameters: 0
Layer: flatten, Parameters: 0
Layer: dense, Parameters: 44859520
Layer: dense_1, Parameters: 387

Total Parameters in the Model: 44878979

##################################
# Fitting the model
# for CNN with dropout and
# batch normalization regularization
##################################
epochs = 100
set_seed()
model_dr_bnr_history = model_dr_bnr.fit(train_gen,
                                        steps_per_epoch=len(train_gen) // batch_size,
                                        validation_steps=len(test_gen) // batch_size, 
                                        validation_data=test_gen, 
                                        epochs=epochs,
                                        verbose=0)

##################################
# Evaluating the model
# for CNN with dropout and
# batch normalization regularization
# on the independent validation set
##################################
model_dr_bnr_y_pred = model_dr_bnr.predict(test_gen)

45/45 [==============================] - 4s 78ms/step

##################################
# Plotting the loss profile
# for CNN with dropout and
# batch normalization regularization
# on the training and validation sets
##################################
plot_training_history(model_dr_bnr_history, 'CNN With Dropout and Batch Normalization Regularization : ')

##################################
# Consolidating the predictions
# for CNN with dropout and
# batch normalization regularization
# on the validation set
##################################
model_dr_bnr_predictions = np.array(list(map(lambda x: np.argmax(x), model_dr_bnr_y_pred)))
model_dr_bnr_y_true = test_gen.classes

##################################
# Formulating the confusion matrix
# for CNN with dropout and
# batch normalization regularization
# on the validation set
##################################
CMatrix = pd.DataFrame(confusion_matrix(model_dr_bnr_y_true, model_dr_bnr_predictions), columns=classes, index =classes)

##################################
# Calculating the model 
# Precision, Recall, F-score and Support
# for CNN with dropout and
# batch normalization regularization
# for each category of the validation set
##################################
plt.figure(figsize=(10, 6))
ax = sns.heatmap(CMatrix, annot = True, fmt = 'g' ,vmin = 0, vmax = 250,cmap = 'icefire')
ax.set_xlabel('Predicted',fontsize = 14,weight = 'bold')
ax.set_xticklabels(ax.get_xticklabels(),rotation =0)
ax.set_ylabel('Actual',fontsize = 14,weight = 'bold') 
ax.set_yticklabels(ax.get_yticklabels(),rotation =0)
ax.set_title('CNN With Dropout and Batch Normalization Regularization : Validation Set Confusion Matrix',fontsize = 16,weight = 'bold',pad=20);

##################################
# Resetting all states generated by Keras
##################################
keras.backend.clear_session()

##################################
# Calculating the model accuracy
# for CNN with dropout and
# batch normalization regularization
# for the entire validation set
##################################
model_dr_bnr_acc = accuracy_score(model_dr_bnr_y_true, model_dr_bnr_predictions)

##################################
# Calculating the model 
# Precision, Recall, F-score and Support
# for CNN with dropout and
# batch normalization regularization
# for the entire validation set
##################################
model_dr_bnr_results_all = precision_recall_fscore_support(model_dr_bnr_y_true, model_dr_bnr_predictions, average='macro',zero_division = 1)

##################################
# Calculating the model 
# Precision, Recall, F-score and Support
# for CNN with dropout and
# batch normalization regularization
# for each category of the validation set
##################################
model_dr_bnr_results_class = precision_recall_fscore_support(model_dr_bnr_y_true, model_dr_bnr_predictions, average=None, zero_division = 1)

##################################
# Consolidating all model evaluation metrics 
# for CNN with dropout and
# batch normalization regularization
##################################
metric_columns = ['Precision','Recall', 'F-Score','Support']
model_dr_bnr_all_df = pd.concat([pd.DataFrame(list(model_dr_bnr_results_class)).T,pd.DataFrame(list(model_dr_bnr_results_all)).T])
model_dr_bnr_all_df.columns = metric_columns
model_dr_bnr_all_df.index = ['COVID', 'Normal', 'Viral Pneumonia','Total']
model_dr_bnr_all_df

	Precision	Recall	F-Score	Support
COVID	0.908714	0.912500	0.910603	240.0
Normal	0.875000	0.845833	0.860169	240.0
Viral Pneumonia	0.842105	0.866667	0.854209	240.0
Total	0.875273	0.875000	0.874994	NaN

##################################
# Consolidating all model evaluation metrics 
# for CNN with dropout and
# batch normalization regularization
##################################
model_dr_bnr_model_list = []
model_dr_bnr_measure_list = []
model_dr_bnr_category_list = []
model_dr_bnr_value_list = []

for i in range(3): 
    for j in range(4):
        model_dr_bnr_model_list.append('CNN_DR_BNR')
        model_dr_bnr_measure_list.append(metric_columns[i])
        model_dr_bnr_category_list.append(model_dr_bnr_all_df.index[j])
        model_dr_bnr_value_list.append(model_dr_bnr_all_df.iloc[j,i])

model_dr_bnr_all_summary = pd.DataFrame(zip(model_dr_bnr_model_list,
                                            model_dr_bnr_measure_list,
                                            model_dr_bnr_category_list,
                                            model_dr_bnr_value_list), 
                                        columns=['CNN.Model.Name',
                                                 'Model.Metric',
                                                 'Image.Category',
                                                 'Metric.Value'])

1.7. Consolidated Findings

1. Details
   • 1.1 Details
     • 1.1.1 Details
       • 1.1.1.1 Details

##################################
# Consolidating all the
# CNN model performance measures
##################################
cnn_model_performance_comparison = pd.concat([model_nr_all_summary, 
                                              model_dr_all_summary,
                                              model_bnr_all_summary, 
                                              model_dr_bnr_all_summary], 
                                             ignore_index=True)

##################################
# Consolidating all the precision
# model performance measures
##################################
cnn_model_performance_comparison_precision = cnn_model_performance_comparison[cnn_model_performance_comparison['Model.Metric']=='Precision']
cnn_model_performance_comparison_precision_CNN_NR = cnn_model_performance_comparison_precision[cnn_model_performance_comparison_precision['CNN.Model.Name']=='CNN_NR'].loc[:,"Metric.Value"]
cnn_model_performance_comparison_precision_CNN_BR = cnn_model_performance_comparison_precision[cnn_model_performance_comparison_precision['CNN.Model.Name']=='CNN_DR'].loc[:,"Metric.Value"]
cnn_model_performance_comparison_precision_CNN_BNR = cnn_model_performance_comparison_precision[cnn_model_performance_comparison_precision['CNN.Model.Name']=='CNN_BNR'].loc[:,"Metric.Value"]
cnn_model_performance_comparison_precision_CNN_DR_BNR = cnn_model_performance_comparison_precision[cnn_model_performance_comparison_precision['CNN.Model.Name']=='CNN_DR_BNR'].loc[:,"Metric.Value"]

##################################
# Combining all the precision
# model performance measures
# for all CNN models
##################################
cnn_model_performance_comparison_precision_plot = pd.DataFrame({'CNN_NR': cnn_model_performance_comparison_precision_CNN_NR.values,
                                                                'CNN_BR': cnn_model_performance_comparison_precision_CNN_BR.values,
                                                                'CNN_BNR': cnn_model_performance_comparison_precision_CNN_BNR.values,
                                                                'CNN_DR_BNR': cnn_model_performance_comparison_precision_CNN_DR_BNR.values},
                                                               index=cnn_model_performance_comparison_precision['Image.Category'].unique())
cnn_model_performance_comparison_precision_plot

	CNN_NR	CNN_BR	CNN_BNR	CNN_DR_BNR
COVID	0.954545	0.962791	0.924686	0.908714
Normal	0.909091	0.847015	0.837302	0.875000
Viral Pneumonia	0.825279	0.881857	0.877729	0.842105
Total	0.896305	0.897221	0.879906	0.875273

##################################
# Plotting all the precision
# model performance measures
# for all CNN models
##################################
cnn_model_performance_comparison_precision_plot = cnn_model_performance_comparison_precision_plot.plot.barh(figsize=(10, 6), width=0.90)
cnn_model_performance_comparison_precision_plot.set_xlim(0.00,1.00)
cnn_model_performance_comparison_precision_plot.set_title("Model Comparison by Precision Performance on Validation Data")
cnn_model_performance_comparison_precision_plot.set_xlabel("Precision Performance")
cnn_model_performance_comparison_precision_plot.set_ylabel("Image Categories")
cnn_model_performance_comparison_precision_plot.grid(False)
cnn_model_performance_comparison_precision_plot.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))
for container in cnn_model_performance_comparison_precision_plot.containers:
    cnn_model_performance_comparison_precision_plot.bar_label(container, fmt='%.5f', padding=-50, color='white', fontweight='bold')

##################################
# Consolidating all the recall
# model performance measures
##################################
cnn_model_performance_comparison_recall = cnn_model_performance_comparison[cnn_model_performance_comparison['Model.Metric']=='Recall']
cnn_model_performance_comparison_recall_CNN_NR = cnn_model_performance_comparison_recall[cnn_model_performance_comparison_recall['CNN.Model.Name']=='CNN_NR'].loc[:,"Metric.Value"]
cnn_model_performance_comparison_recall_CNN_BR = cnn_model_performance_comparison_recall[cnn_model_performance_comparison_recall['CNN.Model.Name']=='CNN_DR'].loc[:,"Metric.Value"]
cnn_model_performance_comparison_recall_CNN_BNR = cnn_model_performance_comparison_recall[cnn_model_performance_comparison_recall['CNN.Model.Name']=='CNN_BNR'].loc[:,"Metric.Value"]
cnn_model_performance_comparison_recall_CNN_DR_BNR = cnn_model_performance_comparison_recall[cnn_model_performance_comparison_recall['CNN.Model.Name']=='CNN_DR_BNR'].loc[:,"Metric.Value"]

##################################
# Combining all the recall
# model performance measures
# for all CNN models
##################################
cnn_model_performance_comparison_recall_plot = pd.DataFrame({'CNN_NR': cnn_model_performance_comparison_recall_CNN_NR.values,
                                                             'CNN_BR': cnn_model_performance_comparison_recall_CNN_BR.values,
                                                             'CNN_BNR': cnn_model_performance_comparison_recall_CNN_BNR.values,
                                                             'CNN_DR_BNR': cnn_model_performance_comparison_recall_CNN_DR_BNR.values},
                                                            index=cnn_model_performance_comparison_recall['Image.Category'].unique())
cnn_model_performance_comparison_recall_plot

	CNN_NR	CNN_BR	CNN_BNR	CNN_DR_BNR
COVID	0.875000	0.862500	0.920833	0.912500
Normal	0.875000	0.945833	0.879167	0.845833
Viral Pneumonia	0.925000	0.870833	0.837500	0.866667
Total	0.891667	0.893056	0.879167	0.875000

##################################
# Plotting all the recall
# model performance measures
# for all CNN models
##################################
cnn_model_performance_comparison_recall_plot = cnn_model_performance_comparison_recall_plot.plot.barh(figsize=(10, 6), width=0.90)
cnn_model_performance_comparison_recall_plot.set_xlim(0.00,1.00)
cnn_model_performance_comparison_recall_plot.set_title("Model Comparison by Recall Performance on Validation Data")
cnn_model_performance_comparison_recall_plot.set_xlabel("Recall Performance")
cnn_model_performance_comparison_recall_plot.set_ylabel("Image Categories")
cnn_model_performance_comparison_recall_plot.grid(False)
cnn_model_performance_comparison_recall_plot.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))
for container in cnn_model_performance_comparison_recall_plot.containers:
    cnn_model_performance_comparison_recall_plot.bar_label(container, fmt='%.5f', padding=-50, color='white', fontweight='bold')

##################################
# Consolidating all the f-score
# model performance measures
##################################
cnn_model_performance_comparison_fscore = cnn_model_performance_comparison[cnn_model_performance_comparison['Model.Metric']=='F-Score']
cnn_model_performance_comparison_fscore_CNN_NR = cnn_model_performance_comparison_fscore[cnn_model_performance_comparison_fscore['CNN.Model.Name']=='CNN_NR'].loc[:,"Metric.Value"]
cnn_model_performance_comparison_fscore_CNN_BR = cnn_model_performance_comparison_fscore[cnn_model_performance_comparison_fscore['CNN.Model.Name']=='CNN_DR'].loc[:,"Metric.Value"]
cnn_model_performance_comparison_fscore_CNN_BNR = cnn_model_performance_comparison_fscore[cnn_model_performance_comparison_fscore['CNN.Model.Name']=='CNN_BNR'].loc[:,"Metric.Value"]
cnn_model_performance_comparison_fscore_CNN_DR_BNR = cnn_model_performance_comparison_fscore[cnn_model_performance_comparison_fscore['CNN.Model.Name']=='CNN_DR_BNR'].loc[:,"Metric.Value"]

##################################
# Combining all the f-score
# model performance measures
# for all CNN models
##################################
cnn_model_performance_comparison_fscore_plot = pd.DataFrame({'CNN_NR': cnn_model_performance_comparison_fscore_CNN_NR.values,
                                                             'CNN_BR': cnn_model_performance_comparison_fscore_CNN_BR.values,
                                                             'CNN_BNR': cnn_model_performance_comparison_fscore_CNN_BNR.values,
                                                             'CNN_DR_BNR': cnn_model_performance_comparison_fscore_CNN_DR_BNR.values},
                                                            index=cnn_model_performance_comparison_fscore['Image.Category'].unique())
cnn_model_performance_comparison_fscore_plot

	CNN_NR	CNN_BR	CNN_BNR	CNN_DR_BNR
COVID	0.913043	0.909890	0.922756	0.910603
Normal	0.891720	0.893701	0.857724	0.860169
Viral Pneumonia	0.872299	0.876310	0.857143	0.854209
Total	0.892354	0.893300	0.879207	0.874994

##################################
# Plotting all the fscore
# model performance measures
# for all CNN models
##################################
cnn_model_performance_comparison_fscore_plot = cnn_model_performance_comparison_fscore_plot.plot.barh(figsize=(10, 6), width=0.90)
cnn_model_performance_comparison_fscore_plot.set_xlim(0.00,1.00)
cnn_model_performance_comparison_fscore_plot.set_title("Model Comparison by F-Score Performance on Validation Data")
cnn_model_performance_comparison_fscore_plot.set_xlabel("F-Score Performance")
cnn_model_performance_comparison_fscore_plot.set_ylabel("Image Categories")
cnn_model_performance_comparison_fscore_plot.grid(False)
cnn_model_performance_comparison_fscore_plot.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))
for container in cnn_model_performance_comparison_fscore_plot.containers:
    cnn_model_performance_comparison_fscore_plot.bar_label(container, fmt='%.5f', padding=-50, color='white', fontweight='bold')

2. Summary

A detailed report was formulated documenting all the analysis steps and findings.

##################################
# Introduction
##################################

##################################
# Methodology
##################################

##################################
# Data Gathering
##################################

##################################
# Data Description
##################################

##################################
# Data Quality Assessment
##################################

##################################
# Data Preprocessing
##################################

##################################
# Data Exploration
##################################

##################################
# Model Development
##################################

##################################
# Overall Findings and Implications
##################################

##################################
# Conclusions
##################################

3. References

• [Book] Deep Learning with Python by Francois Chollet
• [Book] Deep Learning: A Visual Approach by Andrew Glassner
• [Book] Learning Deep Learning by Magnus Ekman
• [Book] Practical Deep Learning by Ronald Kneusel
• [Book] Deep Learning with Tensorflow and Keras by Amita Kapoor, Antonio Gulli and Sujit Pal
• [Book] Deep Learning by John Kelleher
• [Book] Generative Deep Learning by David Foster
• [Book] Deep Learning Illustrated by John Krohn, Grant Beyleveld and Aglae Bassens
• [Book] Neural Networks and Deep Learning by Charu Aggarwal
• [Book] Grokking Deep Learning by Andrew Trask
• [Book] Deep Learning with Pytorch by Eli Stevens, Luca Antiga and Thomas Viehmann
• [Book] Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville
• [Book] Deep Learning from Scratch by Seth Weidman
• [Book] Fundamentals of Deep Learning by Nithin Buduma, Nikhil Buduma and Joe Papa
• [Book] Hands-On Machine Learning with Scikit-Learn, Keras and Tensorflow by Aurelien Geron
• [Book] Deep Learning for Computer Vision by Jason Brownlee
• [Python Library API] numpy by NumPy Team
• [Python Library API] pandas by Pandas Team
• [Python Library API] seaborn by Seaborn Team
• [Python Library API] matplotlib.pyplot by MatPlotLib Team
• [Python Library API] matplotlib.image by MatPlotLib Team
• [Python Library API] matplotlib.offsetbox by MatPlotLib Team
• [Python Library API] tensorflow by TensorFlow Team
• [Python Library API] keras by Keras Team
• [Python Library API] pil by Pillow Team
• [Python Library API] glob by glob Team
• [Python Library API] cv2 by OpenCV Team
• [Python Library API] os by os Team
• [Python Library API] random by random Team
• [Python Library API] keras.models by TensorFlow Team
• [Python Library API] keras.layers by TensorFlow Team
• [Python Library API] keras.wrappers by TensorFlow Team
• [Python Library API] keras.utils by TensorFlow Team
• [Python Library API] keras.optimizers by TensorFlow Team
• [Python Library API] keras.preprocessing.image by TensorFlow Team
• [Python Library API] keras.callbacks by TensorFlow Team
• [Python Library API] keras.metrics by TensorFlow Team
• [Python Library API] sklearn.metrics by Scikit-Learn Team
• [Article] Convolutional Neural Networks, Explained by Mayank Mishra (Towards Data Science)
• [Article] A Comprehensive Guide to Convolutional Neural Networks — the ELI5 way by Sumit Saha (Towards Data Science)
• [Article] Understanding Convolutional Neural Networks: A Beginner’s Journey into the Architecture by Afaque Umer (Medium)
• [Article] Introduction to Convolutional Neural Networks (CNN) by Manav Mandal (Analytics Vidhya)
• [Article] What Are Convolutional Neural Networks? by IBM Team (IBM)
• [Article] What is CNN? A 5 Year Old guide to Convolutional Neural Network by William Ong (Medium)
• [Article] Convolutional Neural Network by Thomas Wood (DeepAI.Org)
• [Article] How Do Convolutional Layers Work in Deep Learning Neural Networks? by Jason Brownlee (Machine Learning Mastery)
• [Article] Convolutional Neural Networks Explained: Using PyTorch to Understand CNNs by Vihar Kurama (BuiltIn)
• [Article] Convolutional Neural Networks Cheatsheet by Afshine Amidi and Shervine Amidi (Stanford University)
• [Article] An Intuitive Explanation of Convolutional Neural Networks by Ujjwal Karn (The Data Science Blog)
• [Article] Convolutional Neural Network by NVIDIA Team (NVIDIA)
• [Article] Convolutional Neural Networks (CNN) Overview by Nikolaj Buhl (Encord)
• [Article] Understanding Convolutional Neural Network (CNN): A Complete Guide by LearnOpenCV Team (LearnOpenCV)
• [Article] Convolutional Neural Networks (CNNs) and Layer Types by Adrian Rosebrock (PyImageSearch)
• [Article] How Convolutional Neural Networks See The World by Francois Chollet (The Keras Blog)
• [Article] What Is a Convolutional Neural Network? by MathWorks Team (MathWorks)
• [Kaggle Project] Covid 19 Radiography Data - EDA and CNN Model by Juliana Negrini De Araujo (Kaggle)
• [Kaggle Project] Pneumonia Detection using CNN (92.6% Accuracy) by Madhav Mathur (Kaggle)
• [Kaggle Project] COVID Detection from CXR Using Explainable CNN by Manu Siddhartha (Kaggle)
• [Kaggle Project] Class Activation Mapping for COVID-19 CNN by Amy Zhang (Kaggle)
• [Kaggle Project] CNN XRay COVID Classification by Gabriel Mino (Kaggle)
• [Kaggle Project] Detecting-Covid-19-Images | CNN by Felipe Oliveira (Kaggle)
• [Kaggle Project] Detection of Covid Positive Cases using DL by Sana Shaikh (Kaggle)
• [Kaggle Project] Deep Learning and Transfer Learning on COVID-19 by Digvijay Yadav (Kaggle)
• [Kaggle Project] X-ray Detecting Using CNN by Shivan Kumar (Kaggle)
• [Kaggle Project] Classification of COVID-19 using CNN by Islam Selim (Kaggle)
• [Kaggle Project] COVID-19 - Revisiting Pneumonia Detection by Paulo Breviglieri (Kaggle)
• [Kaggle Project] Multi-Class X-ray Covid19 Classification-94% Accurary by Derrel Souza (Kaggle)
• [Publication] Gradient-Based Learning Applied to Document Recognition by Yann LeCun, Leon Bottou, Yoshua Bengio and Patrick Haffner (Proceedings of the IEEE)

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